Yapay Sinir Ağı Yaklaşımlarının Yeraltı Suyundaki Nitrat Konsantrasyonunu Tahmin Etme Performansı

Abstract

Su temininde temel kaynaklardan olduğu için yeraltı suyundaki kirlilik konsantrasyonunun tahmini önemlidir. Nitrat (NO3-N) yeraltı suyu kirliliğinde iyi bilinen gösterge parametrelerinden birisidir. Yapay sinir ağları (YSA) geçmiş veriler kullanılarak yeraltı suyundaki nitrat konsantrasyonunu tahmin etmek için kullanılabilir. Bu çalışmada, literatürdeki bir kuyu analizinden türetilen örnek bir veri seti, altı farklı kuyu özelliğine (girdi parametrelerine) göre yeraltı suyunun nitrat konsantrasyonunu (hedef parametre) tahmin etmek için kullanılmıştır. Kuyuların farklı hidrojeolojik parametrelerinin nitrat konsantrasyonu üzerindeki etkisine ilk kez bu çalışmada dikkat çekilmiştir. BPNN ve GRNN olmak üzere iki farklı YSA yaklaşımının performansı, regresyon performansları üzerinden karşılaştırmalı olarak değerlendirilmektedir. YSA modellerinin regresyon sonuçlarına bakıldığında, bu veri seti ile GRNN (R = 0.99) algoritmasının BPNN (R = 0.98) algoritmasından biraz daha iyi çalıştığı sonucuna varılabilir. Korelasyon sonuçları, nitrat kirliliğini tahmin etmek için kuyuların en önemli özelliklerinin sırasıyla kuyu derinliği, su tablasının altındaki derinlik, elek üstü kil ve kuyu ızgarasına derinlik olduğunu göstermektedir. Ayrıca tüm bu özellikler kuyunun nitrat konsantrasyonu ile ters orantılıdır.

Keywords

• Yeraltı Suyu
• Nitrat Kirliliği
• Yapay Sinir Ağı
• Regresyon
• BPNN
• GRNN

The Performance of Artificial Neural Network Approaches to Estimate the Nitrate Concentration in Groundwater

Abstract

The estimation of the pollution concentration in groundwater is important, since it is one of the key resources of water supply. Nitrate (NO3-N) is one of the well-known indicator parameters in groundwater pollution. Using historical data, artificial neural networks can be utilized to estimate the nitrate concentration in groundwater. In this study, a sample dataset, which is derived from a survey analysis in the literature, is used to estimate the nitrate concentration of groundwater (i.e., target parameter) with respect to six different well characteristics (i.e., input parameters). The effect of different hydrogeological parameters of the wells on the nitrate concentration is focused for the first time in this study. The performance of two different ANN approaches, namely BPNN and GRNN, is evaluated comparatively by means of their regression performances. Considering regression results of ANN models, it can be concluded that the GRNN (R=0.99) algorithm works slightly better than the BPNN (R=0.98) algorithm with this dataset. Correlation results indicate that the most important characteristics of the wells to estimate the nitrate pollution are the well depth, depth below water table, clay above screen, and depth to well screen, respectively. Moreover, all these characteristics are inversely related to nitrate concentration of the well.

Keywords

• Groundwater
• Nitrate Pollution
• Artificial Neural Network
• Regression
• BPNN
• GRNN

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